Population distribution modelling at fine spatio-temporal scale based on mobile phone data

References

• Ahas, R., A. Aasa, Y. Yuan, M. Raubal, Z. Smoreda, Y. Liu, C. Ziemlicki, M. Tiru, and M. Zook. 2015. “Everyday Space–Time Geographies: Using Mobile Phone-based Sensor Data to Monitor Urban Activity in Harbin, Paris, and Tallinn.” International Journal of Geographical Information Science 29 (11): 2017–2039. doi:10.1080/13658816.2015.1063151.
   Web of Science ®Google Scholar
• Ahas, R., S. Silm, O. Järv, E. Saluveer, and M. Tiru. 2010. “Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones.” Journal of Urban Technology 17 (1): 3–27. doi:10.1080/10630731003597306.
   Web of Science ®Google Scholar
• Batista e Silva, F., J. Gallego, and C. Lavalle. 2013. “A High-resolution Population Grid Map for Europe.” Journal of Maps 9 (1): 16–28. doi:10.1080/17445647.2013.764830.
   Web of Science ®Google Scholar
• Bengtsson, L., X. Lu, A. Thorson, R. Garfield, and J. von Schreeb. 2011. “Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti.” Plos Medicine 8 (8): 1–9. doi:10.1371/journal.pmed.1001083.
   Web of Science ®Google Scholar
• Binek, J., O. Šerý, H. Svobodová, and Z. Šilhan. 2017. Strategický plán rozvoje městské části Brno-střed na období 2018–2025 [Strategic plan for the development of the Brno-střed for the period 2018–2025]. http://www.brno-stred.cz/uploads/Strategicky_plan/SP_MC_Brno_stred_pro_tisk_9.6.17_.pdf.
   Google Scholar
• Cao, J., W. Tu, Q. Li, and R. Cao. 2017. “Spatio-temporal Analysis of Aggregated Human Activities Based on Massive Mobile Phone Tracking Data.” Journal of Geo-Information Science 19 (4): 467–474. doi:10.3724/SP.J.1047.2017.00467.
   Google Scholar
• CZSO. 2009. Klasifikace stavebních děl [Construction Classification]. Accessed February 14, 2018. https://www.czso.cz/csu/czso/klasifikace_stavebnich_del_-cz_cc-_platna_od_1_10_2009.
   Google Scholar
• CZSO. 2011. Census 2011 Results. Accessed February 24, 2018. https://www.czso.cz/csu/sldb/home; https://www.czso.cz/csu/rso/-registr-scitacich-obvodu-a-budov.
   Google Scholar
• CZSO. 2017. Information Society in Figures 2017. Accessed February 17, 2018. https://www.czso.cz/csu/czso/information-society-in-figures-2014-2016.
   Google Scholar
• Data-Pop Alliance. 2017. “Data-Pop Alliance.” Accessed February 14, 2018. http://datapopalliance.org/.
   Google Scholar
• Deville, P., C. Linard, S. Martine, M. Gilbert, F. R. Stevens, A. E. Gaughan, V. D. Blondel, and A. J. Tatem. 2014. “Dynamic Population Mapping Using Mobile Phone Data.” PNAS 111 (45): 1–6. doi:10.1073/pnas.1408439111.
   Web of Science ®Google Scholar
• Dobrovolný, P., and L. Krahula. 2015. “The Spatial Variability of air Temperature and Nocturnal Urban Heat Island Intensity in the City of Brno, Czech Republic.” Moravian Geographical Reports 23 (3): 8–16. doi:10.1515/mgr-2015-0013.
   Web of Science ®Google Scholar
• Dufková, K., M. Ficek, L. Kencl, J. Novák, J. Kouba, I. Gregor, and J. Danihelka. 2008. “Active GSM Cell-ID Tracking: ‘Where Did You Disappear?’” In Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments, 7–12. doi:10.1145/1410012.1410015.
   Google Scholar
• European Commission Joint Research Centre. 2013. “INSPIRE Data Specification on Buildings – Technical Guidelines.” 323 p. Accessed February 19, 2018. https://inspire.ec.europa.eu/file/1533/download?token=ouzQkBuP.
   Google Scholar
• Eurostat. 2011. Reference Data. Accessed February 12, 2018. http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data.
   Google Scholar
• Gallego, F. J. 2010. “A Population Density Grid of the European Union.” Population and Environment 31 (6): 460–473. doi:10.1007/s11111-010-0108-y.
   Web of Science ®Google Scholar
• Gao, S. 2015. “Spatio-temporal Analytics for Exploring Human Mobility Patterns and Urban Dynamics in the Mobile Age.” Spatial Cognition & Computation: An Interdisciplinary Journal 15 (2): 86–114. doi:10.1080/13875868.2014.984300.
   Web of Science ®Google Scholar
• Geletič, J., M. Lehnert, and P. Dobrovolný. 2016. “Modelled Spatio-temporal Variability of air Temperature in an Urban Climate and its Validation: A Case Study of Brno, Czech Republic.” Hungarian Geographical Bulletin 65 (2): 169–180. doi:10.15201/hungeobull.65.2.7.
   Google Scholar
• GSMWEB. 2018. Non-commercial Server about BTSs and Mobile Operators. Accessed March 25, 2018. http://www.gsmweb.cz/.
   Google Scholar
• Herman, L., J. Russnák, R. Stuchlík, and J. Hladík. 2018. “Visualization of Traffic Offences in the City of Brno (Czech Republic): Achieving 3D Thematic Cartography Through Open Source and Open Data.” In Proceedings of 25th Central European Conference, edited by H. Svobodová, 270–280. Brno: Masaryk University. doi:10.5817/CZ.MUNI.P210-8908-2018.
   Google Scholar
• Järv, O., H. Tenkanen, and T. Toivonen. 2017. “Enhancing Spatial Accuracy of Mobile Phone Data Using Multi-temporal Dasymetric Interpolation.” International Journal of Geographical Information Science 31 (8): 1630–1651. doi:10.1080/13658816.2017.1287369.
   Web of Science ®Google Scholar
• Jonge, E., M. Pelt, and M. Roos. 2012. Time Patterns, Geospatial Clustering and Mobility Statistics Based on Mobile Phone Network Data. Hague: Statistics Netherlands. 26 p. Accessed February 21, 2018. https://www.cbs.nl/NR/rdonlyres/4EDB51ED-927A-4A69-B8F3-4DC57A44DDE4/0/Timepatternsgeospatialclusteringandmobilitystatistics.pdf.
   Google Scholar
• Kang, C., S. Gao, X. Lin, Y. Xiao, Y. Yuan, Y. Liu, and X. Ma. 2010. “Analyzing and Geo-visualizing Individual Human Mobility Patterns Using Mobile Call Records.” In 2010 18th International Conference on Geoinformatics, 1–7. doi:10.1109/GEOINFORMATICS.2010.5567857.
   Google Scholar
• Kang, C., Y. Liu, X. Ma, and L. Wu. 2012. “Towards Estimating Urban Population Distributions from Mobile Call Data.” Journal of Urban Technology 19 (4): 3–21. doi:10.1080/10630732.2012.715479.
   Web of Science ®Google Scholar
• Kang, C., Y. Liu, and L. Wu. 2017. “An Analysis of Entropy of Human Mobility from Mobile Phone Data.” Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University 42 (1): 63–69. doi:10.1320.
   Google Scholar
• Konečný, M., and W. Reinhardt. 2010. “Early Warning and Disaster Management: The Importance of Geographic Information (Part A).” International Journal of Digital Earth 3 (3): 217–220. doi:10.1080/17538947.2010.508884.
   Web of Science ®Google Scholar
• Kressler, F., and K. Steinnocher. 2008. “Object-oriented Analysis of Image and LiDAR Data and Its Potential for a Dasymetric Mapping Application.” In Object-Based Image Analysis, edited by T. Blaschke, S. Lang, and G. J. Hay, 611–624. Berlin, Heidelberg: Springer.
   Google Scholar
• Kubíček, P., E. Mulíčková, M. Konečný, and J. Kučerová. 2011. “Flood Management and Geoinformation Support Within the Emergency Cycle (EU Example).” In Environmental Software Systems. Frameworks of Environment, edited by J. Hřebíček, G. Schimak, and R. Denzer, 77–86. Berlin: Springer. doi:10.1007/978-3-642-22285-6_9.
   Google Scholar
• Lane, N. D., E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. T. Campbell. 2010. “A Survey of Mobile Phone Sensing.” IEEE Communications Magazine 48 (9): 140–150. doi:10.1109/MCOM.2010.5560598.
   Web of Science ®Google Scholar
• Li, Q., and B. Zhou. 2015. “Smartphone-based Individual Indoor Spatiotemporal Behavior Analysis.” Progress in Geography 34 (4): 457–465. doi:10.11820/dlkxjz.2015.04.007.
   Google Scholar
• Lískovec, R. 2016. “Výzkum časoprostorových rytmů prostřednictvím geolokalizačních technologií [Exploration of Timespace Rhythms Using Geolocalization Technologies].” Bachelor thesis. Brno: Masaryk University. 51 p.
   Google Scholar
• Liu, Y., C. Kang, and F. Wang. 2014. “Towards Big Data-driven Human Mobility Patterns and Models.” Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University 39 (6): 660–666. doi:10.13203/j.whugis20140149.
   Google Scholar
• Martin, D. 1996. “An Assessment of Surface and Zonal Models of Population.” International Journal of Geographic Information Systems 10 (8): 973–989. doi:10.1080/02693799608902120.
   Web of Science ®Google Scholar
• Martin, D., S. Cockings, and S. Leung. 2015. “Developing a Flexible Framework for Spatiotemporal Population Modeling.” Annals of the Association of American Geographers 105 (4): 754–772. doi:10.1080/00045608.2015.1022089.
   Web of Science ®Google Scholar
• Mennis, J. 2003. “Generating Surface Models of Population Using Dasymetric Mapping.” The Professional Geographer 55 (1): 31–42. doi:10.1111/0033–0124.10042.
   Web of Science ®Google Scholar
• Mesarec, B., and M. Lep. 2009. “Combining the Grid-based Spatial Planning and Network-based Transport Planning.” Technological and Economic Development of Economy 15 (1): 60–77. doi:10.3846/1392-8619.2009.15.60-77.
   Web of Science ®Google Scholar
• Mulíček, O., R. Osman, and D. Seidenglanz. 2015. “Urban Rhythms: A Chronotopic Approach to Urban Timespace.” Time & Society 24 (3): 304–325. doi:10.1177/0961463X14535905.
   Web of Science ®Google Scholar
• Nagle, N. N., B. P. Buttenfield, S. Leyk, and S. Spielman. 2014. “Dasymetric Modeling and Uncertainty.” Annals of the Association of American Geographers 104 (1): 80–95. doi:10.1080/00045608.2013.843439.
   PubMed Web of Science ®Google Scholar
• Novák, J., and J. Temelová. 2012. “Každodenní život a Prostorová Mobilita Mladých Pražanů: Pilotní Studie Využití Lokalizačních dat Mobilních Telefonů [Everyday Life and Spatial Mobility of Young People in Prague: A Pilot Study Using Mobile Phone Location Data].” Sociologický Časopis / Czech Sociological Review 48 (5): 911–938.
   Web of Science ®Google Scholar
• Ouředníček, M., J. Nemeškal, P. Špačková, M. Hampl, and J. Novák. 2018. “A Synthetic Approach to the Delimitation of the Prague Metropolitan Area.” Journal of Maps 14 (1): 26–33. doi:10.1080/17445647.2017.1422446.
   Web of Science ®Google Scholar
• Oxendine, C. E., E. Schnebele, G. Cervone, and N. Waters. 2014. “Fusing Non-authoritative Data to Improve Situational Awareness in Emergencies.” In Proceedings of the 11th International ISCRAM Conference, edited by S. R. Hiltz, et al., 760–764. University Park: ISCRAM. doi:10.13140/2.1.4980.5443.
   Google Scholar
• Pastor-Escuredo, D., A. Morales-Guzmán, et al. 2014. “Flooding Through the Lens of Mobile Phone Activity.” In IEEE Global Humanitarian Technology Conference, 1–8. San Jose: IEEE. doi:10.1109/GHTC.2014.6970293.
   Google Scholar
• Paulus, F., A. Krömer, J. Petr, and J. Černý. 2016. Analýza hrozeb pro Českou republiku [Threats Analysis for the Czech Republic]. Lázně Bohdaneč: IOOL. 9 p. Accessed February 20, 2018. www.hzscr.cz/soubor/analyza-hrozeb-zprava-pdf.aspx.
   Google Scholar
• Peng, Z., Z. Sun, Q. Cheng, H. Jiao, and W. Chen. 2018. “Urban Land Use Function Recognition Method Using Sequential Mobile Phone Data.” Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University 43 (9): 1399–1407. 1437. doi:10.13203/j.whugis20170329.
   Google Scholar
• Pucci, P., F. Manfredini, and P. Tagliolato. 2015. Mapping Urban Practices Through Mobile Phone Data. 1st ed. New York: Springer International Publishing. 90 p. doi:10.1007/978-3-319-14833-5.
   Google Scholar
• Reibel, M., and M. E. Bufalino. 2005. “Street-weighted Interpolation Techniques for Demographic Count Estimation in Incompatible Zone Systems.” Environment and Planning A 37 (1): 127–139. doi:10.1068/a36202.
   Web of Science ®Google Scholar
• Řezník, T., B. Horáková, and D. Janiurek. 2011. “Emergency Support System: Actionable Real-time Intelligence with Fusion Capabilities and Cartographic Displays.” Advances in Military Technology 6 (2): 83–97.
   Google Scholar
• Řezník, T., B. Horáková, and R. Szturc. 2015. “Advanced Methods of Cell Phone Localization for Crisis and Emergency Management Applications.” International Journal of Digital Earth 8 (4): 259–272. doi:10.1080/17538947.2013.860197.
   Web of Science ®Google Scholar
• Russnák, J., and L. Herman. 2017. “Visualization and Spatial Analysis of Open Data for Evacuation Planning – Pilot Study of the Dukovany Nuclear Power Plant (Czech Republic).” In 17th International Multidisciplinary Scientific GeoConference SGEM 2017, Informatics, Geoinformatics and Remote Sensing (Vol. 17, Is. 23), 853–860. doi:10.5593/sgem2017/23/S11.107.
   Google Scholar
• Russnák, J., P. Ondrejka, L. Herman, P. Kubíček, and A. Mertel. 2016. “Visualization and Spatial Analysis of Police Open Data as a Part of Community Policing in the City of Pardubice (Czech Republic).” Annals of GIS 22 (3): 187–201. doi:10.1080/19475683.2016.1200670.
   Google Scholar
• Scholz, J., M. Andorfer, and M. Mittlboeck. 2013. “Spatial Accuracy Evaluation of Population Density Grid Disaggregations with Corine Landcover.” In Geographic Information Science at the Heart of Europe, edited by D. Vandenbroucke, B. Bucher, and J. Crompvoets, 267–283. Cham: Springer. doi:10.1007/978-3-319-00615-4_15.
   Google Scholar
• Seidenglanz, D., V. Toušek, and F. Chvátal. 2013. Odborná analýza vývoje počtu osob oficiálně neregistrovaných ve městě Brně [Analysis of the Number of Persons Officially Unregistered in the City of Brno]. https://www.brno.cz/fileadmin/user_upload/sprava_mesta/Strategie_pro_Brno/dokumenty/Odhad_13.pdf.
   Google Scholar
• Silm, S., and R. Ahas. 2010. “The Seasonal Variability of Population in Estonian Municipalities.” Environment and Planning A 42 (10): 2527–2546. doi:10.1068/a43139.
   Web of Science ®Google Scholar
• Slocum, T. 1999. Thematic Cartography and Visualization. 1st ed, 293. Upper Saddle River, NJ: Prentice Hall.
   Google Scholar
• Smith, A., D. Martin, and S. Cockings. 2016. “Spatio-Temporal Population Modelling for Enhanced Assessment of Urban Exposure to Flood Risk.” Applied Spatial Analysis and Policy 9 (2): 145–163. doi:10.1007/s12061-014-9110-6.
   Web of Science ®Google Scholar
• Stachoň, Z., P. Kubíček, R. Štampach, L. Herman, J. Russnák, and M. Konečný. 2016. “Cartographic Principles for Standardized Cartographic Visualization for Crisis Management Community.” In Proceedings, 6th International Conference on Cartography and GIS (Vol. 1 and Vol. 2), edited by T. Bandrova and M. Konečný, 781–788. Sofia: Bulgarian Cartographic Association.
   Google Scholar
• Steenbruggen, J., E. Tranos, and P. Nijkamp. 2015. “Data from Mobile Phone Operators: A Tool for Smarter Cities?” Telecommunications Policy 39 (3–4): 335–346. doi:10.1016/j.telpol.2014.04.001.
   Web of Science ®Google Scholar
• Sýkora, L., and O. Mulíček. 2014. “Prague: Urban Growth and Regional Sprawl.” In Confronting Suburbanization: Urban Decentralization in Postsocialist Central and Eastern Europe, edited by Kiril Stanilov and Luděk Sýkora, 133–162. Chichester: Wiley Blackwell.
   Google Scholar
• Čtyroký, J. 2017. “Kde má Praha centrum? [Where is Prague Center?].” Sborník příspěvků - Konference GIS Esri v ČR. p. 4. Accessed February 21, 2018. http://download.arcdata.cz/konf/2017/sbornik_konference_2017.pdf.
   Google Scholar
• Verkasalo, H., and H. Hämmäinen. 2007. “A Handset-based Platform for Measuring Mobile Service Usage.” Info 9 (1): 80–96. doi:10.1108/14636690710725085.
   Google Scholar
• Wright, J. K. 1936. “A Method of Mapping Densities of Population: With Cape Cod as an Example.” Geographical Review 26 (1): 103–110. doi: 10.2307/209467
   Google Scholar
• Wu, J., L. Huang, Y. Liu, J. Peng, W. Li, S. Gao, and C. Kang. 2012. “Traffic Flow Simulation Based on Call Detail Records.” Dili Xuebao/Acta Geographica Sinica 67 (12): 1657–1665.
   Google Scholar
• Xie, Z. 2006. “A Framework for Interpolating the Population Surface at Residential-housing-unit Level.” GIScience and Remote Sensing 43 (3): 1–19. doi:10.2747/1548-1603.43.3.233.
   Web of Science ®Google Scholar
• Xu, J., Z. Fang, S. Xiao, and L. Yin. 2015. “The Spatio-temporal Heterogeneity Analysis of Massive Urban Mobile Phone Users’ Stay Behavior: A Case Study of Shenzhen City.” Journal of Geo-Information Science 17 (2): 197–205. doi:10.3724/SP.J.1047.2015.00197.
   Google Scholar
• Yuan, Y., and M. Raubal. 2016. “Analyzing the Distribution of Human Activity Space from Mobile Phone Usage: an Individual and Urban-Oriented Study.” International Journal of Geographical Information Science 30 (8): 1594–1621. doi:10.1080/13658816.2016.1143555.
   Web of Science ®Google Scholar
• Yuan, Y., M. Raubal, and Y. Liu. 2012. “Correlating Mobile Phone Usage and Travel Behavior – A Case Study of Harbin, China.” Computers, Environment and Urban Systems 36 (2): 118–130. doi:10.1016/j.compenvurbsys.2011.07.003.
   Web of Science ®Google Scholar
• Zhou, B., Q. Li, Q. Mao, W. Tu, and X. Zhang. 2015. “Activity Sequence-Based Indoor Pedestrian Localization Using Smartphones.” IEEE Transactions on Human-Machine Systems 45 (5): 562–574. doi:10.1109/THMS.2014.2368092.
   Web of Science ®Google Scholar
• Zhou, X., Y. Yue, A. G. O. Yeh, H. Wang, and T. Zhong. 2014. “Uncertainty in Spatial Analysis of Dynamic Data – Identifying City Center.” Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University 39 (6): 701–705. doi:10.13203/j.whugis20140074.
   Google Scholar